Data‐driven prediction of prolonged air leak after video‐assisted thoracoscopic surgery for lung cancer: Development and validation of machine‐learning‐based models using real‐world data through the ePath system

Data‐driven prediction of prolonged air leak after video‐assisted thoracoscopic surgery for lung cancer: Development and validation of machine‐learning‐based models using real‐world data through the ePath system

Abstract Introduction The reliability of data‐driven predictions in real‐world scenarios remains uncertain. This study aimed to develop and validate a machine‐learning‐based model for predicting clinical outcomes using real‐world data from an electronic clinical pathway (ePath) system. Methods All a...

Full description

Saved in:
Bibliographic Details
Main Authors: Saori Tou, Koutarou Matsumoto, Asato Hashinokuchi, Fumihiko Kinoshita, Hideki Nakaguma, Yukio Kozuma, Rui Sugeta, Yasunobu Nohara, Takanori Yamashita, Yoshifumi Wakata, Tomoyoshi Takenaka, Kazunori Iwatani, Hidehisa Soejima, Tomoharu Yoshizumi, Naoki Nakashima, Masahiro Kamouchi
Format: Article
Language:English
Published: Wiley 2025-04-01
Series:Learning Health Systems
Subjects:
data‐driven prediction
electronic clinical pathway
lung cancer
machine learning
prolonged air leak
real‐world data
Online Access:https://doi.org/10.1002/lrh2.10469
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items